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Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes ( EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1 , and TIMP3 ) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes ( NR2F1 and NR2F2 ). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes.

Background Feed-forward loops (FFLs), consisting of miRNAs, transcription factors (TFs) and their common target genes, have been validated to be important for the initialization and development of complex diseases, including cancer. Esophageal Carcinoma (ESCA) and Stomach Adenocarcinoma (STAD) are two types of malignant tumors in the digestive tract. Understanding common and distinct molecular mechanisms of ESCA and STAD is extremely crucial. Results In this paper, we presented a computational framework to explore common and distinct FFLs, and molecular biomarkers for ESCA and STAD. We identified FFLs by combining regulation pairs and RNA-seq data. Then we constructed disease-specific co-expression networks based on the FFLs identified. We also used random walk with restart (RWR) on disease-specific co-expression networks to prioritize candidate molecules. We identified 148 and 242 FFLs for these two types of cancer, respectively. And we found that one TF, E2F3 was related to ESCA, two genes, DTNA and KCNMA1 were related to STAD, while one TF ESR1 and one gene KIT were associated with both of the two types of cancer. Conclusions This proposed computational framework predicted disease-related biomolecules effectively and discovered the correlation between two types of cancers, which helped develop the diagnostic and therapeutic strategies of Esophageal Carcinoma and Stomach Adenocarcinoma.

Background Although great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors. Methods In this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes. Results We got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed. Conclusions The results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers ( NDUFS5 , NDUFA1 , NDUFA13 , and NDUFB8 ) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.

Background Single-cell sequencing approaches allow gene expression to be measured at the single-cell level, providing opportunities and challenges to study the aetiology of complex diseases, including cancer. Methods Based on single-cell gene and lncRNA expression levels, we proposed a computational framework for cell type identification that fully considers cell dropout characteristics. First, we defined the dropout features of the cells and identified the dropout clusters. Second, we constructed a differential co-expression network and identified differential modules. Finally, we identified cell types based on the differential modules. Results The method was applied to single-cell melanoma data, and eight cell types were identified. Enrichment analysis of the candidate cell marker genes for the two key cell types showed that both key cell types were closely related to the physiological activities of the major histocompatibility complex (MHC); one key cell type was associated with mitosis-related activities, and the other with pathways related to ten diseases. Conclusions Through identification and analysis of key melanoma-related cell types, we explored the molecular mechanism of melanoma, providing insight into melanoma research. Moreover, the candidate cell markers for the two key cell types are potential therapeutic targets for melanoma.

Background Cleft lip (CL) is one of the most common congenital birth defects with complex etiology. While genome-wide association studies (GWAS) have made significant advances in our understanding of mutations and their related genes with potential involvement in the etiology of CL, it remains unknown how these genes are functionally regulated and interact with each other in lip development. Currently, identifying the disease-causing genes in human CL is urgently needed. So far, the causative CL genes have been largely undiscovered, making it challenging to design experiments to validate the functional influence of the mutations identified from large genomic studies such as CL GWAS. Results Transcription factors (TFs) and microRNAs (miRNAs) are two important regulators in cellular system. In this study, we aimed to investigate the genetic interactions among TFs, miRNAs and the CL genes curated from the previous studies. We constructed miRNA-TF co-regulatory networks, from which the critical regulators as putative drivers in CL were examined. Based on the constructed networks, we identified ten critical hub genes with prior evidence in CL. Furthermore, the analysis of partitioned regulatory modules highlighted a number of biological processes involved in the pathology of CL, including a novel pathway “Signaling pathway regulating pluripotency of stem cells”. Our subnetwork analysis pinpointed two candidate miRNAs, hsa-mir-27b and hsa-mir-497 , activating the Wnt pathway that was associated with CL. Our results were supported by an independent gene expression dataset in CL. Conclusions This study represents the first regulatory network analysis of CL genes. Our work presents a global view of the CL regulatory network and a novel approach on investigating critical miRNAs, TFs and genes via combinatory regulatory networks in craniofacial development. The top genes and miRNAs will be important candidates for future experimental validation of their functions in CL.

Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the heterogeneity among the domain ontologies to a certain extent. This paper proposes a probability-based and similarity-based ontology mapping algorithm, the purpose of which is to calculate the similarity between the concepts of the multi-domain ontology. Using the ESA algorithm based on Wikipedia and the principle that the similarity between the concepts with the same name equals 1, the paper proposes a new concept, ontology mapping association graph, to represent mapping results. The experiments show that the accuracy rate of the probability-based and similarity-based ontology mapping algorithm can reach 80% on both two Chinese test sets, namely, WordSimilarity-353 and Words-240. Compared with other algorithms, it does stand out on the aspect of accuracy.

Nowadays, cervical cancer is a leading cause of death among women. Determining the gene signature is one of the major issues in bioinformatics. Though many of the methodologies and applications have been given as suggestions in recent literature, efficient techniques, which may be considered complex gene expression profiles, will be able to find out the most relevant signatures required. In the given article, we demonstrate a new framework to find out the dense module-based gene signatures (DeMoS) and their targeting miRNAs using the quasi-clique detection algorithm and discuss their application in the field of prognostic survival studies. We used a cervical cancer data repository with prognostic clinical data to conduct this experiment. At first, we executed the empirical Bayes test by applying the linear model for the microarray method to find out the dysregulated genes, or miRNAs. MiRNA-mediated dysregulated target genes were pulled out of the particular dysregulated miRNAs. After that, we discovered densely co-expressed modules by applying a quasi-clique identification technique. The average correlation coefficient has been computed for each resultant module, and the module that contains the highest correlation was composed as the resultant gene signature (10-gene signatures containing ten genes are as follows: FGF9, FGF18, PPP1R9A, ERBB4, DCDC2, TOX3, ARMC3, DNALI1, RGL3, and ENPP3). After that, we applied 10-fold cross-validation to three common classifiers (SVM, PAM, and random forest) and obtained the AUC. (0.95 for SVM, 0.955 for RF, and 0.955 for PAM) that is better than the state-of-the-art algorithms (Li et al. in Technol Cancer Res Treat 17:1533033818767455, 2017/2018; Huang et al. in Cancer 117(15):3363–3373, 2011). In addition to it, we found eight dysregulated miRNAs that have targeted the gene, as mentioned earlier. At last, we performed a prognosis survival study for the resultant gene signature (i.e., containing the p-value of Cox regression as 4.2e - 02). DEMOS is very useful for determining the signature for any of the microarray or RNA-Seq profiles. The code is available at https://github.com/sahasuparna/DeMoS .

Despite several algorithms for searching subgraphs in motif detection presented in the literature, no effort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumerate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size motif detection, and ESU has its own advantage in small-size motif detection.

Elective single embryo transfer (eSET) has been increasingly advocated, but concerns about the lower pregnancy rate after reducing the number of embryos transferred have encouraged transfer of multiple embryos. Extended embryo culture combined with electively freezing all embryos and undertaking a deferred frozen embryo transfer might increase pregnancy rate after eSET. We aimed to establish whether elective frozen single blastocyst transfer improved singleton livebirth rate compared with fresh single blastocyst transfer. This multicentre, non-blinded, randomised controlled trial was undertaken in 21 academic fertility centres in China. 1650 women with regular menstrual cycles undergoing their first cycle of in-vitro fertilisation were enrolled from Aug 1, 2016, to June 3, 2017. Eligible women were randomly assigned to either fresh or frozen single blastocyst transfer. The randomisation sequence was computer generated, with block sizes of two, four, or six, stratified by study site. For those assigned to frozen blastocyst transfer, all blastocysts were cryopreserved and a delayed frozen-thawed single blastocyst transfer was done. The primary outcome was singleton livebirth rate. Analysis was by intention to treat. This trial is registered at the Chinese Clinical Trial Registry, number ChiCTR-IOR-14005405. 825 women were assigned to each group and included in analyses. Frozen single blastocyst transfer resulted in higher rates of singleton livebirth than did fresh single blastocyst transfer (416 [50%] vs 329 [40%]; relative risk [RR] 1·26, 95% CI 1·14–1·41, p<0·0001). The risks of moderate or severe ovarian hyperstimulation syndrome (four of 825 [0·5%] in frozen single blastocyst transfer vs nine of 825 [1·1%] in fresh single blastocyst transfer; p=0·16), pregnancy loss (134 of 583 [23·0%] vs 124 of 481 [25·8%]; p=0·29), other obstetric complications, and neonatal morbidity were similar between the two groups. Frozen single blastocyst transfer was associated with a higher risk of pre-eclampsia (16 of 512 [3·1%] vs four of 401 [1·0%]; RR 3·13, 95% CI 1·06–9·30, p=0·029). Frozen single blastocyst transfer resulted in a higher singleton livebirth rate than did fresh single blastocyst transfer in ovulatory women with good prognosis. The increased risk of pre-eclampsia after frozen blastocyst transfer warrants further studies. The National Key Research and Development Program of China.

Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender’s secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack.